Classes
class	ConvergenceOptions

class	LSOptions

class	BFGSMinimizer

class	ModelAdaptor

class	BFGSLineSearch

class	BFGSUpdate_HInv

class	LBFGSUpdate
	Implement a limited memory version of the BFGS update. More...

Typedefs
typedef Eigen::Matrix< double, Eigen::Dynamic, Eigen::Dynamic >	matrix_d

typedef Eigen::Matrix< double, Eigen::Dynamic, 1 >	vector_d

Enumerations
enum	TerminationCondition { TERM_SUCCESS = 0, TERM_ABSX = 10, TERM_ABSF = 20, TERM_RELF = 21, TERM_ABSGRAD = 30, TERM_RELGRAD = 31, TERM_MAXIT = 40, TERM_LSFAIL = -1 }

Functions
template<typename M >
double	lp_no_jacobian (const M &model, std::vector< double > &params_r, std::vector< int > &params_i, std::ostream *o=0)

template<typename Scalar >
Scalar	CubicInterp (const Scalar &df0, const Scalar &x1, const Scalar &f1, const Scalar &df1, const Scalar &loX, const Scalar &hiX)
	Find the minima in an interval [loX, hiX] of a cubic function which interpolates the points, function values and gradients provided. More...

template<typename Scalar >
Scalar	CubicInterp (const Scalar &x0, const Scalar &f0, const Scalar &df0, const Scalar &x1, const Scalar &f1, const Scalar &df1, const Scalar &loX, const Scalar &hiX)
	Find the minima in an interval [loX, hiX] of a cubic function which interpolates the points, function values and gradients provided. More...

template<typename FunctorType , typename Scalar , typename XType >
int	WolfeLineSearch (FunctorType &func, Scalar &alpha, XType &x1, Scalar &f1, XType &gradx1, const XType &p, const XType &x0, const Scalar &f0, const XType &gradx0, const Scalar &c1, const Scalar &c2, const Scalar &minAlpha)
	Perform a line search which finds an approximate solution to: $\min_\alpha f(x_0 + \alpha p)$ satisfying the strong Wolfe conditions: 1) $f(x_0 + \alpha p) \leq f(x_0) + c_1 \alpha p^T g(x_0)$ 2) $\vert p^T g(x_0 + \alpha p) \vert \leq c_2 \vert p^T g(x_0) \vert$ where $g(x) = \frac{\partial f}{\partial x}$ is the gradient of f(x). More...

template<typename M >
double	newton_step (M &model, std::vector< double > &params_r, std::vector< int > &params_i, std::ostream *output_stream=0)

Typedef Documentation

typedef Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic> stan::optimization::matrix_d

Definition at line 14 of file newton.hpp.

typedef Eigen::Matrix<double, Eigen::Dynamic, 1> stan::optimization::vector_d

Definition at line 15 of file newton.hpp.

Enumeration Type Documentation

enum stan::optimization::TerminationCondition

Enumerator
TERM_SUCCESS
TERM_ABSX
TERM_ABSF
TERM_RELF
TERM_ABSGRAD
TERM_RELGRAD
TERM_MAXIT
TERM_LSFAIL

Definition at line 21 of file bfgs.hpp.

Function Documentation

template<typename Scalar >

Scalar stan::optimization::CubicInterp	(	const Scalar &	df0,
		const Scalar &	x1,
		const Scalar &	f1,
		const Scalar &	df1,
		const Scalar &	loX,
		const Scalar &	hiX
	)

Find the minima in an interval [loX, hiX] of a cubic function which interpolates the points, function values and gradients provided.

Implicitly, this function constructs an interpolating polynomial g(x) = a_3 x^3 + a_2 x^2 + a_1 x + a_0 such that g(0) = 0, g(x1) = f1, g'(0) = df0, g'(x1) = df1 where g'(x) = 3 a_3 x^2 + 2 a_2 x + a_1 is the derivative of g(x). It then computes the roots of g'(x) and finds the minimal value of g(x) on the interval [loX,hiX] including the end points.

This function implements the full parameter version of CubicInterp().

Parameters

df0	First derivative value, f'(x0)
x1	Second point
f1	Second function value, f(x1)
df1	Second derivative value, f'(x1)
loX	Lower bound on the interval of solutions
hiX	Upper bound on the interval of solutions

Definition at line 35 of file bfgs_linesearch.hpp.

template<typename Scalar >

Scalar stan::optimization::CubicInterp	(	const Scalar &	x0,
		const Scalar &	f0,
		const Scalar &	df0,
		const Scalar &	x1,
		const Scalar &	f1,
		const Scalar &	df1,
		const Scalar &	loX,
		const Scalar &	hiX
	)

Find the minima in an interval [loX, hiX] of a cubic function which interpolates the points, function values and gradients provided.

Implicitly, this function constructs an interpolating polynomial g(x) = a_3 x^3 + a_2 x^2 + a_1 x + a_0 such that g(x0) = f0, g(x1) = f1, g'(x0) = df0, g'(x1) = df1 where g'(x) = 3 a_3 x^2 + 2 a_2 x + a_1 is the derivative of g(x). It then computes the roots of g'(x) and finds the minimal value of g(x) on the interval [loX,hiX] including the end points.

Parameters

x0	First point
f0	First function value, f(x0)
df0	First derivative value, f'(x0)
x1	Second point
f1	Second function value, f(x1)
df1	Second derivative value, f'(x1)
loX	Lower bound on the interval of solutions
hiX	Upper bound on the interval of solutions

Definition at line 104 of file bfgs_linesearch.hpp.

template<typename M >

double stan::optimization::lp_no_jacobian	(	const M &	model,
		std::vector< double > &	params_r,
		std::vector< int > &	params_i,
		std::ostream *	o = `0`
	)

Definition at line 288 of file bfgs.hpp.

template<typename M >

double stan::optimization::newton_step	(	M &	model,
		std::vector< double > &	params_r,
		std::vector< int > &	params_i,
		std::ostream *	output_stream = `0`
	)

Definition at line 34 of file newton.hpp.

template<typename FunctorType , typename Scalar , typename XType >

int stan::optimization::WolfeLineSearch	(	FunctorType &	func,
		Scalar &	alpha,
		XType &	x1,
		Scalar &	f1,
		XType &	gradx1,
		const XType &	p,
		const XType &	x0,
		const Scalar &	f0,
		const XType &	gradx0,
		const Scalar &	c1,
		const Scalar &	c2,
		const Scalar &	minAlpha
	)

Perform a line search which finds an approximate solution to:

$\min_\alpha f(x_0 + \alpha p)$

satisfying the strong Wolfe conditions: 1) $f(x_0 + \alpha p) \leq f(x_0) + c_1 \alpha p^T g(x_0)$ 2) $\vert p^T g(x_0 + \alpha p) \vert \leq c_2 \vert p^T g(x_0) \vert$ where $g(x) = \frac{\partial f}{\partial x}$ is the gradient of f(x).

Template Parameters

FunctorType A type which supports being called as ret = func(x,f,g) where x is the input point, f and g are the function value and gradient at x and ret is non-zero if function evaluation fails.

Parameters

func	Function which is being minimized.
alpha	First value of $\alpha$ to try. Upon return this contains the final value of the $\alpha$ .
x1	Final point, equal to $x_0 + \alpha p$ .
f1	Final point function value, equal to $f(x_0 + \alpha p)$ .
gradx1	Final point gradient, equal to $g(x_0 + \alpha p)$ .
p	Search direction. It is assumed to be a descent direction such that .
x0	Value of starting point, .
f0	Value of function at starting point, .
gradx0	Value of function gradient at starting point, .
c1	Parameter of the Wolfe conditions. Typically c1 = 1e-4.
c2	Parameter of the Wolfe conditions. Typically c2 = 0.9.
minAlpha	Smallest allowable step-size.

Returns: Returns zero on success, non-zero otherwise.

Definition at line 225 of file bfgs_linesearch.hpp.

Classes

Typedefs

Enumerations

Functions

Typedef Documentation

Enumeration Type Documentation

Function Documentation